Multiclass semisupervised learning based upon kernel spectral clustering

IEEE Transactions on Neural Networks and Learning Systems
Siamak MehrkanoonJohan A K Suykens

Abstract

This paper proposes a multiclass semisupervised learning algorithm by using kernel spectral clustering (KSC) as a core model. A regularized KSC is formulated to estimate the class memberships of data points in a semisupervised setting using the one-versus-all strategy while both labeled and unlabeled data points are present in the learning process. The propagation of the labels to a large amount of unlabeled data points is achieved by adding the regularization terms to the cost function of the KSC formulation. In other words, imposing the regularization term enforces certain desired memberships. The model is then obtained by solving a linear system in the dual. Furthermore, the optimal embedding dimension is designed for semisupervised clustering. This plays a key role when one deals with a large number of clusters.

References

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Citations

Sep 1, 2015·Neural Networks : the Official Journal of the International Neural Network Society·Siamak MehrkanoonJohan A K Suykens
Apr 28, 2016·Biomedical Engineering Online·Andres Orozco-DuqueGerman Castellanos-Dominguez
Apr 15, 2016·IEEE Transactions on Neural Networks and Learning Systems·Chen GongJie Yang
May 24, 2017·IEEE Transactions on Neural Networks and Learning Systems·Ruxin WangDacheng Tao
Aug 8, 2017·IEEE Transactions on Neural Networks and Learning Systems·Siamak Mehrkanoon, Johan A K Suykens

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